Data Science Leader, AIML Data Operations

Summary

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Do you love thinking analytically? Are you passionate about solving complex business problems in a fast-paced environment? The AIML Data Operations group engages with teams across Apple’s ecosystem with the ultimate goal of delivering high-quality annotated data in support of unreleased products and ground breaking AI technology.

We are currently seeking an experienced and influential Data Science Leader, AIML Data Operations to grow the AIML Data Operations Annotations Analytics team and function. This position will lead a team of data engineers and scientists to establish top-line health metrics, identify key growth drivers, and recommend operational and business optimizations through scalable, responsive, and interpretable research and analyses.

Description

The ideal candidate for this role is an experienced manager with deep expertise in analytics and experimentation, excels at building strong cross-functional relationships to drive data-informed decisions across the company, and is skilled at leading teams to surface and communicate key data insights that improve performance and customer experience at a global scale.

Responsibilities

• Establish a center of excellence for Data Operations Data Science team by uncovering business-actionable insights in collaboration across our customer groups as well as the Operations team.
• Oversee large complex projects from conception to completion, develop roadmaps and requirements, identify risks and develop contingency plans, evaluate impact, and regularly communicate status to executives.
• Establish, enhance, and socialize key operational metrics that accurately represent the business state of health.
• Lead proactive analyses identifying key drivers of metrics, and make recommendations that optimize performance.
• Develop reporting measures to expand the portfolio of self-service dashboards and reports to inform, enable, and empower relevant stakeholders.
• Draft schema and instrumentation requirements to enrich operational datasets for new projects, etc.
• Identify key factors that lead to improving productivity and quality.
• Build a holistic view of analyst behaviors across the various platforms, and identify synergies that drive a more positive analyst experience and engagement.
• Develop, recruit, and train a diverse team of high-performing data engineers and scientists focused on uncovering insights from large-scale data across all aspects of the operation.

Minimum Qualifications

• Bachelors degree in Computer Science, Statistics, Mathematics, Engineering, Economics or related field.
• 4+ years of experience in managing data science, analytics, or data operations teams.
• 4+ years of experience in data science with proven skills in developing meaningful and concise analytic objectives from general business goals.
• Tested capabilities and comfort in scalable schema designs, relational database and big data technologies, ETL, code management, and query performance optimization.
• Mastery in SQL-based languages, and proficiency in at least one large-scale data languages.
• Strong hands-on experience interpretable with machine learning models and sophisticated analytic solutions using scripting tools such as Python or R.

Preferred Qualifications

• Masters degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Economics or related field.
• Experience with the deployment of Large Language Models / Generative AI in service of efficiency in operations.
• Excellent communication and presentation skills with meticulous attention to detail and the ability to collaborate effectively between business and analytic teams at multiple levels of the organization.
• Experience in managing data science or analytics teams in AI & ML annotations and collections areas.
• Passion for AIML and Operations, with a consistent track record of operational results.

Key skills/competency

• Data Science Leadership
• Analytics Management
• Operational Optimization
• Machine Learning
• SQL
• Python
• R
• ETL
• Big Data Technologies
• Cross-functional Collaboration